👁️ MultimodalID:
rag-multimodalMultimodal RAG (cross-module)
多模态 RAG (跨模块)
🎯Core Definition
multimodal RAG uses CLIP-style multimodal embeddings to unify images and text in one vector space for joint retrieval, followed by reranking (cross-encoder) and generation.
💡Use Cases
image/chart/PDF QA and multimodal knowledge-base retrieval interviews.
⚡Key Problems Solved
text-only retrieval cannot capture information inside images, charts, or tables; multimodal embeddings plus reranking substantially improve recall. See the AI_Engineering module: vector search, recall, and rerank pipelines in the AI_Engineering module (guide naive-and-advanced-rag).
🎯5 High-Frequency Exam Points
1
How does multimodal RAG unify images and text into one vector space for joint retrieval?
2
Multimodal embeddings (e.g. CLIP) vs text-only embeddings for retrieval?
3
How to design recall/rerank pipelines for mixed image-text content?
4
Where does multimodal RAG beat text-only RAG the most?
5
Hard cases for table/chart document retrieval and common solutions?
📖 In-depth Guide:📄 naive-and-advanced-rag →
Updated 2026-08-12🎯
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